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agrawal-priyank

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Built classifiers using logistic regression and decision trees to classify product reviews and used machine learning techniques such as boosting, precision and recall, and stochastic gradient descent for optimization in Python

machine-learning-regression

Built house price prediction model using linear regression and k nearest neighbors and used machine learning techniques like ridge, lasso, and gradient descent for optimization in Python

Jupyter Notebook

machine-learning-clustering-retrieval

Built text and image clustering models using unsupervised machine learning algorithms such as nearest neighbors, k means, LDA , and used techniques such as expectation maximization, locality sensitive hashing, and gibbs sampling in Python

Jupyter Notebook

Web-Scraper-Sentiment-Analysis-TripAdvisor

Academic project for Advances in Data Science and Architecture course

Job-Portal-Full-Stack-Web-Application

Academic project for Web Development Tools and Methods course

JavaScript

Restaurant-Recommender-System-AWS-Hadoop-MapReduce

Academic project for Advance Database Management Systems (Big-Data) course

Java

Multithreaded-Simulations

Academic assignments of the Foundations of Parallel, Concurrent, and Multithreaded Programming course

Java

machine-learning-case-studies

Built prediction and retrieval models for document retrieval, image retrieval, house price prediction, song recommendation, and analyzed sentiments using machine learning algorithms in Python

Jupyter Notebook

Machine-Learning-Models-For-Insurance-Loan-Applicants

Academic project for Advances in Data Science and Architecture course

python-for-data-science-and-machine-learning

Practice exercises completed using different machine learning packages in Python

Jupyter Notebook

agrawal-priyank/machine-learning-classification

agrawal-priyank

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